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Recently, studies on single image super-resolution using Deep Convolutional Neural Networks (DCNN) have been demonstrated to have made outstanding progress over conventional signal-processing based methods. However, existing architectures have grown wider and deeper, resulting in a large amount of computation and memory cost, but only a small improvement in performance. To address this issue, in this paper, we present a Wavelet-and Saak-transform Dual Path Network (WSDPN), which considers notdoi:10.1109/access.2020.2997028 fatcat:etllfwvjnjgldggq5sebpmtmoy